autokeras: StructuredDataClassifier's exported model can't predict or evaluate

Bug Description

I use autokeras to train a classifier, I can predict or evaluate with the <autokeras.tasks.structured_data.StructuredDataClassifier>. But the model exported by the export_model function can’t predict or evaluate. When I try model.evaluate(x_valid,y_valid)it raise the error below, and model.evaluate(x_valid,y_valid) raise the same error. No matter which dataset I use, the iteration ends at 32.

 32/2000 [..............................] - ETA: 48s
---------------------------------------------------------------------------
UnimplementedError                        Traceback (most recent call last)
<ipython-input-20-73f41c3caa9d> in <module>
----> 1 model.evaluate(x_valid,y_valid)

c:\users\exia\appdata\local\programs\python\python36\lib\site-packages\tensorflow_core\python\keras\engine\training.py in evaluate(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing)
    928         max_queue_size=max_queue_size,
    929         workers=workers,
--> 930         use_multiprocessing=use_multiprocessing)
    931 
    932   def predict(self,

c:\users\exia\appdata\local\programs\python\python36\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in evaluate(self, model, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
    488         sample_weight=sample_weight, steps=steps, callbacks=callbacks,
    489         max_queue_size=max_queue_size, workers=workers,
--> 490         use_multiprocessing=use_multiprocessing, **kwargs)
    491 
    492   def predict(self, model, x, batch_size=None, verbose=0, steps=None,

c:\users\exia\appdata\local\programs\python\python36\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _model_iteration(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
    473               mode=mode,
    474               training_context=training_context,
--> 475               total_epochs=1)
    476           cbks.make_logs(model, epoch_logs, result, mode)
    477 

c:\users\exia\appdata\local\programs\python\python36\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
    126         step=step, mode=mode, size=current_batch_size) as batch_logs:
    127       try:
--> 128         batch_outs = execution_function(iterator)
    129       except (StopIteration, errors.OutOfRangeError):
    130         # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?

c:\users\exia\appdata\local\programs\python\python36\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
     96     # `numpy` translates Tensors to values in Eager mode.
     97     return nest.map_structure(_non_none_constant_value,
---> 98                               distributed_function(input_fn))
     99 
    100   return execution_function

c:\users\exia\appdata\local\programs\python\python36\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
    566         xla_context.Exit()
    567     else:
--> 568       result = self._call(*args, **kwds)
    569 
    570     if tracing_count == self._get_tracing_count():

c:\users\exia\appdata\local\programs\python\python36\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
    636               *args, **kwds)
    637       # If we did not create any variables the trace we have is good enough.
--> 638       return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds)  # pylint: disable=protected-access
    639 
    640     def fn_with_cond(*inner_args, **inner_kwds):

c:\users\exia\appdata\local\programs\python\python36\lib\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs)
   1609          if isinstance(t, (ops.Tensor,
   1610                            resource_variable_ops.BaseResourceVariable))),
-> 1611         self.captured_inputs)
   1612 
   1613   def _call_flat(self, args, captured_inputs, cancellation_manager=None):

c:\users\exia\appdata\local\programs\python\python36\lib\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1690       # No tape is watching; skip to running the function.
   1691       return self._build_call_outputs(self._inference_function.call(
-> 1692           ctx, args, cancellation_manager=cancellation_manager))
   1693     forward_backward = self._select_forward_and_backward_functions(
   1694         args,

c:\users\exia\appdata\local\programs\python\python36\lib\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager)
    543               inputs=args,
    544               attrs=("executor_type", executor_type, "config_proto", config),
--> 545               ctx=ctx)
    546         else:
    547           outputs = execute.execute_with_cancellation(

c:\users\exia\appdata\local\programs\python\python36\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     65     else:
     66       message = e.message
---> 67     six.raise_from(core._status_to_exception(e.code, message), None)
     68   except TypeError as e:
     69     keras_symbolic_tensors = [

c:\users\exia\appdata\local\programs\python\python36\lib\site-packages\six.py in raise_from(value, from_value)

UnimplementedError:  Cast double to string is not supported
	 [[node Cast (defined at <ipython-input-20-73f41c3caa9d>:1) ]] [Op:__inference_distributed_function_1731]

Function call stack:
distributed_function

Reproducing Steps

clf = ak.StructuredDataClassifier(max_trials=10)
clf.fit(x_train, y_train,validation_data=(x_valid, y_valid))
model = clf.export_model()
model.evaluate(x_valid,y_valid)

where x_train is a (5000, 4) numpy.ndarray, y_train is a (5000, 1) numpy.ndarray x_valid is a (2000, 4) numpy.ndarray, y_valid is a (2000, 1) numpy.ndarray no matter I use model.evaluate(x_valid,y_valid) or model.evaluate(x_train,y_train) it raise the same error.

Setup Details

Include the details about the versions of:

• OS type and version: Win10
• Python: 3.6.5
• autokeras: 1.0.1
• scikit-learn:0.22
• numpy:1.18.0
• scipy:1.4.1
• tensorflow:2.1.0